Machine Learning Operationalisation (ML Ops) is a set of practices that aim to quickly and reliably build, deploy and monitor machine learning applications. Many organizations standardize around certain tools to develop a platform to enable these goals.
One combination of tools includes using Databricks to build and manage machine learning models and Kubernetes to deploy models. This article will explore how to design this solution on Microsoft Azure followed by step-by-step instructions on how to implement this solution as a proof-of-concept.
This article is targeted towards:
Organizations looking to build and manage machine learning models on Databricks.
Organizations that have experience deploying and managing Kubernetes workloads.
Organizations looking to deploy workloads that require low latency and interactive model predictions (e.g. a product recommendation API).
A GitHub repository with more details can be found here.